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IntraFace has an expression detector built in. However, if you would like to do a custom smile detector, then what you should do is to convert your spatial facial fiducial points to some kind of a feature vector, which could be input to a non-linear classifier such as SVM or MLP. A trivial way to do this would be to use angular relations (if the face is ...

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You would greatly benefit from knowledge in signal processing, but some specific useful knowledge would be in computer vision and estimation (like Kalman filters or alpha beta filters). With respect to tracking blobs, you could use blob detector algorithms to find meaningful blobs, use some descriptor like SIFT to describe the blob and then do feature ...

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There are several alternatives: Alternative #1: you need to find for each cell of the tessellation the list of pixels contained in the cell. To do that, you can use a rasterization algorithm. You can use my open-source implementation . If performance is an issue, you can also do that with a GPU. Alternative #2 (the simplest): (if you have many different ...

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The answer to quantify the difficulty is not trivial to give. It depends already hugely on your $C$ programming skills. As $C$ is not accounted to be an easy language, a more trivial answer is not so easy. Is there a reason to not use $C++$? You can still write $C$-style code in $C++$ and are not forced to use object oriented coding style if you want to ...

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This should probably be a comment rather than an answer but I can't comment... If you can define your curve geometry (2) with some function like (its just an example, it can of course be modified) : $$f(x) = (a x + b) \theta(x-x_0) + \left(\sqrt{R^2 - (x+x_c)^2} - y_c\right)\theta(x_0-x)$$ where $a,b,x_0,x_c,y_c$ are free parameters such that $x_0$ is ...

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If there's some model for the signal for $t < t_0$ and $t > t_f$, perhaps it would be possible to try to fit the full signal using a Metropolis-Hastings or Goodman-Weare algorithm. This could then also yield some information about the probability distributions for the parameters involved.

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